• DocumentCode
    1844572
  • Title

    Cascade Linear SVM for Object Detection

  • Author

    Song, Jinze ; Wu, Tao ; An, Ping

  • Author_Institution
    Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defence Technol., Changcha
  • fYear
    2008
  • fDate
    18-21 Nov. 2008
  • Firstpage
    1755
  • Lastpage
    1759
  • Abstract
    This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.
  • Keywords
    image classification; learning (artificial intelligence); object detection; quadratic programming; support vector machines; cascade linear SVM classifier; machine learning; object detection; quadratic programming; Automation; Detectors; Educational institutions; Face detection; Los Angeles Council; Mechatronics; Object detection; Risk management; Support vector machine classification; Support vector machines; Cascade; object detection; support vector machine (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
  • Conference_Location
    Hunan
  • Print_ISBN
    978-0-7695-3398-8
  • Electronic_ISBN
    978-0-7695-3398-8
  • Type

    conf

  • DOI
    10.1109/ICYCS.2008.173
  • Filename
    4709239